Choices over time: methodological issues in investigating current change 1

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1 Pre-publication draft from Aarts, Close, Leech and Wallis (eds.) (2013) The English Verb Phrase, Cambridge: CUP. Choices over time: methodological issues in investigating current change 1 1 Introduction Bas Aarts, a Joanne Close b and Sean Wallis a a University College London and b University of Leeds The fact that English is changing is immediately apparent to a modern reader of, say, 18th or 19th century literature, or indeed to a teenager speaking to an elderly relative. However, as Mair (2006) points out, anecdotal evidence for linguistic change is unreliable. The systematic study of language change requires large, evenly balanced, and reliably annotated corpora with texts sampled over a period of time. These considerations are accepted by many linguists working on current changes in English. However, with regard to methodology we observe that within the field of diachronic corpus linguistics there are still a number of issues that generate a certain amount of discussion and debate. One of these concerns the issue of variability. Bauer (1994: 19) highlights the importance of this concept in studies of language change when he states that change is impossible without some variation. Variation within a set of linguistic choices, including the idea that there may be competition between these variants, is fundamental to studies in current change. In this paper we will argue that an important methodological task for corpus linguists studying language change is to focus on linguistic variation where there is a choice. Many factors are likely to influence the use of particular words, phrases or constructions. If we wish to study and explain variation found in a corpus as being the result of factors affecting variation over time, then we need to eliminate as many potential alternative sources of variation as possible. This, we contend, calls for a restricted definition of the variants involved in a perceived change, and a consideration of any knock-out contexts, i.e. contexts where variation may be impossible, or constrained in a different manner to the general case. We use the Diachronic Corpus of Present-day Spoken English (DCPSE) as a database. This corpus is unique in two important respects: it exclusively contains spoken English and is fully parsed, and as such is suitable for studying current change in English from the late 1950s to the early 1990s. It complements other resources, including major historical corpora of writing, notably A Representative Corpus of Historical English Registers (ARCHER) 2 which contains written texts sampled from the late 17th to the late 20th century, as well as corpora of earlier speech derived from written sources such as A Corpus of English Dialogues (CED; Kytö and Culpeper 2006) and the Old Bailey Corpus (Huber 2007). 3 In the next section we briefly present the functionality of DCPSE. 2 The Diachronic Corpus of Present-Day Spoken English The Diachronic Corpus of Present-Day Spoken English (DCPSE) is a diachronic corpus with a difference: it spans a time period of approximately thirty years and is composed of material from spoken English. DCPSE is composed of speech samples collected between the late 1950s and the early 1990s, and it allows us to monitor grammatical changes during this period. In this paper we will present data on the alternation between shall and will and the increasing use of the progressive construction, with a focus on the methodological issues raised by these studies. Before showing how this can be done with DCPSE we will discuss a few general features of the corpus. DCPSE was released by the Survey of English Usage (SEU) in It contains 464,074 words of orthographic (word-for-word) transcriptions of English speech taken from the London-Lund 1 Early versions of this paper were presented at the symposium Current Change in the English Verb Phrase celebrating the fiftieth anniversary of the Survey of English Usage (July 2009), and at the workshop Corpus-based advances in historical linguistics organised by the Philological Society, University of York (November 2009). We are grateful to a number of colleagues for comments: Geoffrey Leech, Manfred Krug, Jill Bowie and an anonymous reader. We acknowledge with gratitude the support of the Arts and Humanities Research Council under grant AH/E006299/1. 2 See: 3 See:

2 Corpus (LLC), 4 and words of spoken data from the British Component of the International Corpus of English (ICE-GB, Nelson,Wallis and Aarts 2002). 5 These are sampled in matching text categories, so there is approximately the same quantity of face-to-face conversation (for example) in both portions ( subcorpora ). Two caveats are in order. The LLC subcorpus is distributed over a longer period of time (20 years) than the ICE-GB subcorpus (three years), and texts are not evenly distributed by year. DCPSE includes mostly spontaneous spoken English, such as face-to-face conversations, telephone conversations, various types of discussions and debates, legal cross-examinations, business transactions, speeches and interviews. As it is generally assumed that changes in English propagate themselves in the first instance through spontaneous discourse, we would argue that DCPSE is ideal for the study of current change. Whereas written corpora contain text genres which allow for editorial correction, DCPSE consists entirely of orthographically transcribed utterances. Immediate selfcorrection is explicitly marked, so that repetitions and word partials can be excluded from searches. The small proportion of scripted speech that is included is transcribed, rather than the script reproduced. The spoken transcription is divided into putative sentence utterances, termed text units. Every text unit is then given a full grammatical analysis in the form of a phrase structure tree using a grammar based on Quirk et al. (1985) and exemplified in Figure 1. DCPSE contains over 87,000 such fully analysed text units. These trees were produced by automatic and manual parsing methods and were then extensively cross-checked. Parsing natural language is notoriously difficult, and naturally occurring spoken English especially so. There was a substantial manual editing effort, which raises the issue of consistency (Wallis 2003). To deal with this we employed extensive cross-checking in the construction of DCPSE, applying our experience with the annotation of the ICE-GB corpus to the LLC subcorpus. The result is a corpus of spoken English which allows a high degree of confidence in the reliability and completeness of the grammatical analysis. Figure 1: An example tree diagram, I think that s fascinating [DI-A02 #28]. 6 The question arises of how to search this forest of over 87,000 trees. Our ICECUP software (International Corpus of English Corpus Utility Program; Nelson, Wallis and Aarts 2002) is designed as a platform for exploring the corpus and obtaining results. Linguists can search for lexical strings, 4 The LLC is the spoken part of the Survey of English Usage Corpus, founded by Randolph Quirk in It contains 510,576 words of 1960s spoken English, is prosodically annotated, and has been used and continues to be used by many scholars for their research. 5 ICE-GB is composed of both spoken and written material from the 1990s. It contains textual markup, and is fully grammatically annotated. All the sentences/utterances in the corpus are assigned a tree structure. 6 In this tree diagram each lexical item, phrase and clause is associated with a node which contains function information (top left), form information (top right), as well as features (bottom portion). Trees can be drawn in a number of orientations. Here we use a left-to-right visualization for space-efficiency reasons. PU=parse unit, CL=clause, main=main, SU=subject, NP=noun phrase, NPHD=NP head, PRON=pronoun, pers=personal, VB=verbal, VP=verb phrase, MVB= main verb, V=verb, montr=monotransitive, OD=direct object, depend=dependent, dem=demonstrative, cop=copular, CS=Subject Complement, AJP=adjective phrase, prd=predicative, AJHD=adjective phrase head, ingp= ing participle.

3 wild cards, etc., and importantly in grammatical studies of current change tree patterns. ICECUP contains a powerful query system, termed Fuzzy Tree Fragments (FTFs). FTFs are sketches of grammatical constructions that can be applied to the corpus to obtain an exhaustive set of matching cases. Figure 2 shows an example of an FTF which matches all instances of a VP followed by a subject complement (CS). 7 This FTF matches the three nodes highlighted in Figure 1 above. Figure 2: An FTF created with ICECUP, matching the highlighted nodes in Figure 1. Respecting the fact that linguists disagree about grammar, ICECUP allows users to experiment with the best way of retrieving the grammatical phenomena they are interested in, using the Quirk-style representation in the corpus. The interface is designed to let linguists construct FTFs, apply them to the corpus, identify how they match cases in the corpus, and refine their queries. One can also select part of a tree structure and construct an FTF query from that fragment in order to find how a particular lexical string is analysed, and then seek all similar analyses. ICECUP offers a range of search tools based around this idea of an abstract FTF query, including a lexicon and grammaticon. DCPSE is an unparalleled resource for linguists interested in short-term changes in spoken English, and in this paper we will demonstrate its value in studies of current change using the examples of the progressive and the shall vs. will alternation. 8 3 Focusing on true alternation: the progressive For decades, research in the field of sociolinguistics has highlighted the importance of the linguistic variant (see Labov 1969). This impetus has percolated into historical studies of language, but is often overlooked in corpus linguistics. Many studies on current change that have been carried out using corpora have collected frequencies for lexical items or grammatical constructions, but often without considering these frequencies alongside the variants of these patterns as part of a bigger picture. In the next three sections we look at a number of methodologies for exploring change. First we look at an approach which measures change in the progressive construction using normalised frequency counts. In section 3.2 we then look at a measure which investigates frequency changes as a percentage of the total number of VPs. Section 3.3 considers changes within a set of variants. 3.1 Changes in frequency per million words Leech (2003) and Smith (2003) both investigate changes in the modal system of English. They carry out a series of independent log-likelihood goodness of fit tests for the item, 9 in this case a modal auxiliary, against the number of words in the corpus, using a method owing to Rayson (2003). This tests whether a perceived difference in a distribution d is too large to be explained by accident. 7 While the grammar that underlies the ICE-GB parsing (Quirk et al. 1985) conceives of Verb Phrases as only containing verbs (see Figure 2), in this paper the focus will be on the extended VP, i.e. a verb + dependents, as we discuss later. 8 For more information see Aarts et al. (1998), and Nelson et al. (2002). 9 Log likelihood (G 2 ) is best thought of as a different χ 2 test. It employs a different formula but obtains a similar result. See Appendix 1, and

4 First we will apply Rayson s method to progressive VPs, which can be easily identified in DCPSE (cf. Aarts, Close and Wallis 2010). 10 The method compares the distribution in Column A with that of Column B in Table 1. A: item VP(prog) B: words C: rate per million words D: increase d % (LLC = 100%) LLC (1960s) 2, ,063 6,406 (0.64%) ICE-GB (1990s) 3, ,986 7,824 (0.78%) % ±5.48% TOTAL 6, ,049 7,081 (0.71%) Table 1: Change over time of VP(prog) as a proportion of the number of words. In this table and the tables below, d is cited with a 95% confidence interval indicated by the ± value. We compare Column A with B using the goodness of fit log-likelihood test. This attempts to see if the ratio between LLC and ICE-GB frequency counts in Column A is similar enough (as defined by the test) to the ratio between the same counts in Column B. In this instance the results are significant at an error level of p<0.05. The observed increase in Column C (from 6,406 to 7,824) is likely to represent a real (non-zero) increase in the population of comparably sampled English utterances. We can also measure the percentage difference d % between the rate for ICE-GB and that found in the LLC subcorpus (column D). We apply the following formula: (E1) percentage swing d % = p2 p1, p 1 where p 1 represents the probability of selecting a given item (in this example the main verb in a progressive context), at random from the first subcorpus (LLC), and p 2 the same probability in the second subcorpus (ICE-GB). Note that we could substitute any normalised frequency rate per word, per thousand words, or per million words for probability here. We can also compute a Gaussian (Normal) confidence interval (Wallis 2010) for d % at a given error level. This obtains d % = ±5.48 percent at the 0.05 level, or, to put it another way, there is a 19 out of 20 chance that the observed increase d % is between percent and percent. 3.2 Changes in frequency as a percentage of the total number of VPs In a POS-tagged corpus, normalising frequency counts by reporting frequencies per million words is a perfectly reasonable procedure, and obtaining word counts for subcorpora is a simple operation. However, not all words are equally substitutable with the object of study (our Column A). Language is not, to misquote Elbert Hubbard, just one damn word after another and corpora are not a random sample of words (Wallis 2010). In addition, speakers and text genres may vary in how verbal they are. Verb phrase density may be uneven. Bowie, Wallis and Aarts (forthcoming) show that VP density varies substantially in DCPSE in two important ways: by genre between 110,000 and nearly 160,000 VPs per million words in various genres and, in some genres, over time. This variation is obscured by the fact that, averaged over the LLC and ICE-GB subcorpora, VP density does not change. In formal face-to-face conversations VP density increases over time by between 8.66 and percent (at a 95 percent confidence interval). However, in informal conversations and telephone calls, VP density does not significantly increase between the 1960s and 1990s. Therefore if the progressive is used in certain genres more frequently than others, the opportunity to use the progressive must also vary, simply due to this variation in VP density. 10 This method also picks up tag question progressives such as Burning the candle at both ends are you? [DI-A18 #162]. The preceeding VP has the feature prog although the auxiliary may not be included in the VP.

5 When we evaluate rates of progressive VP use, it is more accurate to consider changes in the rate per VP than in the rate per n lexical words. By taking this step we remove this VP density variation, and thereby eliminate the possibility that an observed change could be due to changes in VP density. The revised calculation looks something like the following. A: item VP(prog) B: VP C: rate p (proportion) D: increase d % (LLC = 100%) LLC (1960s) 2,973 63, % ICE-GB (1990s) 3,294 57, % % ±5.46% TOTAL 6, , % Table 2: Change over time of VP(prog) as a proportion of the number of VPs. Note that we have replaced citations per million words in Column C with the simple proportion p (this does not affect the overall calculation). Our results obtain a similar increase (d % ) to Table 1, but we have eliminated the possibility that variation in VP density accounted for our results. Changing the baseline frequency from words to an overarching grammatical class (such as VPs) can have a dramatic effect on results. For example, Aarts, Wallis and Bowie (forthcoming) plotted d % values for modal auxiliaries can, may, etc. from DCPSE on a per million word and per modal basis and showed that results differed markedly can rose as a proportion of all modals, but did not change significantly with respect to word frequency; could, would and should all fell with respect to word frequency, but this fall could not be distinguished from an overall decline in modal use. 3.3 Changes in one choice out of a set of alternants Ideally, we wish to evaluate how the progressive changes over time where the speaker has the option of using this construction. The aim should be to focus our experiment on the set of true alternants to which the item in Column A belongs by removing as many distracting factors as possible. In this set of alternants, variation can be hypothesised to take place between members of the set, i.e. such that they compete and substitute for one another over time (Wallis 2003). A study of modal auxiliaries should ideally therefore distinguish between semantic subcategories (deontic, epistemic, etc.) to identify the particular set of alternants at any given juncture. It could also take into account other competing variants to modals, such as semi-modals or adverbial expressions. Drilling down to sets of true alternants can be onerous if particular distinctions (e.g. modal semantics) are not represented in the corpus (see, for example, Close and Aarts 2010 on the modal must). We return to this question in Section 4. Identifying a set of true alternants is often easier said than done. In Aarts, Close and Wallis (2010) we investigated DCPSE to show that the use of the progressive is increasing. The first step, that of focusing on VPs, is easily achieved (see above). Isolating variants is less straightforward. The optimum alternation pattern is between verb phrases which are progressive and those that could plausibly be turned into a progressive form (but were not) (Figure 3). We might call the resulting ideal set the set of progressivisable VPs. It is simple to obtain the set of progressive VPs from DCPSE using an FTF which searches for all VPs marked with the progressive feature ( VP(prog) ). The crucial step is to identify this progressivisable subset of VPs. Smitterberg (2005: 45-8) identifies a number of contexts in which verb phrases cannot be progressivised, including imperatives, non-finite VPs, and the be going to future construction. Finally it is possible that the set itself may vary over time. Language may contain new innovations, and therefore new alternants, so linguists should ideally incorporate new alternants into

6 their class of progressivisable VPs at the point of their first citation, although these novel cases are unlikely to be sufficiently common to make a difference to an experimental outcome. 11 all words better { true alternates VP( prog) VP progressivisable VP VP(prog) Figure 3: Descending the space of possible choices to focus on true alternants. As should be clear from the foregoing, the process of identifying variants is, in part, subjective, and hence an approximation, and any experimentalist engaging in excluding material must explicitly state their assumptions. For Smitterberg, removing knock-out contexts from the dataset was not straightforward, so his final calculation of the progressive to non-progressive ratio, which he refers to as the S-coefficient, is a percentage of all finite non-imperative verb phrases (excluding be going to + infinitive constructions with future reference) that are in the progressive (Smitterberg 2005: 48). 12 In Aarts, Close and Wallis (2010) we excluded only imperatives and instances of the be going to future. Note that, subject to the limitations of available data, it is entirely legitimate to subdivide an experiment into a series of sub-experiments in order to investigate the rising use of progressive in stative situations only, explore interrogatives only, and so forth. In Column A we have simply retrieved all cases of VPs marked as progressive ( VP(prog) ). After elimination of Smitterberg s knock-out factors, with the exception of non-finite VPs for reasons discussed in Aarts, Close and Wallis (2010:156, fn8), 13 we narrow down the scope of Column B to this restricted set of progressivisable VPs (indicated by VP(+prog) ) in Table 3. Focusing on alternants allows us to estimate the true rate of use (Column C) more precisely, and therefore the trend identified is more meaningful. Again, this is not simply a repeat of the previous result. We have eliminated a potential alternative hypothesis remaining from the previous table, namely that the observed increase in the progressive (as a proportion of all VPs) is explained by a corresponding decline in the proportion of knock-out factors. A: item VP(prog) B: VP(+prog) C: rate p (proportion) D: increase d % (LLC = 100%) LLC (1960s) 2,973 62, % ICE-GB (1990s) 3,294 57, % % ±5.31% TOTAL 6, , % 11 Geoff Leech (personal communication) asks whether this amounts to accepting that the class of progressivisable VPs cannot be built into a model of language change. Our response would be to say that the concept of progressivisable VPs, i.e. VPs that could be given a progressive form without violence to their meaning, must necessarily be defined in some way by enumerating types to either include (cf. will/shall in the next section) or exclude ( knock-out factors ). If a new type is found in the future we must decide whether or not to include this in our definition. 12 Non-finite verb phrases were also excluded by Smitterberg because they were difficult to retrieve automatically and because it is possible that there are other factors that constrain variation in non-finite VPs. 13 In Aarts, Close and Wallis (2010: 156) stative situations were included on the grounds that it is possible for some stative verbs to be progressivised in present day English[0] (see Smith 2000:96). We also included copula constructions and nonfinite verb phrases, because checking individual cases would be needed for accuracy as both can be progressivised in some (but not all) instances (compare: Joan is (*being) tall and Joan is (being) friendly; she pretended to (be) sleep(ing) and she continued to (?be) sleep(?ing)).

7 Table 3: Change over time of VP(prog) as a proportion of the number of progressivisable VPs ( VP(+prog) ). In the case of the progressive, our three baselines turn out to be closely aligned over time. However this does not discount the importance of focusing the experiment as far as possible on the choice. Focusing eliminates the possibility that other sources of variation (e.g. between text genres, or sampling variation) that have an impact on higher order elements in Figure 3, are causing an observed trend, or indeed, as we shall see, obscuring a trend that might be revealed. Smitterberg (2005) found that focusing on progressivisable VPs obtained a different rank order of progressive use between written text genres. Bowie, Wallis and Aarts (this volume) found that the subclass of tensed, pastmarked VPs provide a more meaningful baseline for a study of the perfect construction than all VPs. In identifying semantic alternants we may aggregate grammatically disparate terms. Close and Aarts (2010) investigate the decline of must by comparing the frequency of must against the frequency of the semi-modals have to and have got to. 14 In what follows we carry out a quite different case study of linguistic alternation and demonstrate that these same principles apply. 4 A case study: the alternation shall versus will 4.1 Background Modal verbs have attracted a lot of attention in the current change literature and shall and will are no exception. In 1964 Charles Barber wrote: [T]he distinctions formerly made between shall and will are being lost, and will is coming increasingly to be used instead of shall. One reason for this is that in speech we very often say neither [will] nor [shall], but just [ ll]: I ll see you to-morrow, we ll meet you at the station, John ll get it for you. We cannot use this weak form in all positions (not at the end of a phrase, for example), but we use it very often; and, whatever its historical origin may have been (probably from will), we now use it indiscriminately as a weak form for either shall or will; and very often the speaker could not tell you which he had intended. There is thus often a doubt in a speaker s mind whether will or shall is the appropriate form; and, in this doubt, it is will that is spreading at the expense of shall, presumably because will is used more frequently than shall anyway, and so is likely to be the winner in a levelling process. So people nowadays commonly say or write I will be there, we will all die one day, and so on, when they intend to express simple futurity and not volition. (Barber 1964: 134) Similarly, David Denison has remarked that: During the latter part of our period [1776-present day]... in the first person SHALL has increasingly been replaced by WILL even where there is no element of volition in the meaning. (Denison 1998: 167) Comments such as these may lead us to expect that investigating the trajectory of such a change is straightforward. However, from these two quotations alone a number of interrelated issues arise. These are: (i) the status of the variants; (ii) their syntactic behaviour; and (iii) the intended meaning of the clause. In the following discussion, we will address each of these issues. 14 The authors did not include gotta as there were no examples in the corpus and only one example of got to. This could be due to the way the corpus was transcribed. As Geoff Leech has pointed out to us (personal communication): gotta is not a linguistically well-defined entity. An investigation of the same phenomena in American varieties would undoubtedly include gotta in the pool of variants. The same may be necessary of British varieties in future if gotta spreads in use.

8 4.2 Mair and Leech s work on written English Recently, Mair and Leech (2006: 327) reported frequency statistics for the perceived decline of the use of shall. Their data are based on raw frequency statistics of shall and will in written British and American English (henceforth BrE and AmE, respectively) from the 1960s and 1990s using the Brown family of written English corpora (LOB, F-LOB; Brown, Frown; see Smith and Leech this volume). Counts include verb and negative contractions: e.g., won t and ll are included under will. British English US English 1960s 1990s d % 1960s 1990s d % will 2,798 2, % will 2,702 2, % shall % shall % Total 3,153 2, % Total 2,969 2, % Table 4: Decline in the use of shall in written corpora, LOB/F-LOB and Brown/Frown. (After Mair and Leech 2006.) This table shows that, comparing four one million word corpora, the frequency of will appears to decrease by 2.7 and 11 percent in the BrE and AmE corpora, respectively, and the use of shall by almost 44 percent overall in both BrE and AmE corpora. Mair and Leech employ a goodness of fit log-likelihood test comparing absolute frequencies against the overall word count (see Section 3.1) to confirm that this fall in shall is statistically significant. However, as we have noted, this statement simply tells us that shall is significantly less frequent as a proportion of words in the later dataset. This is not particularly instructive, not least because there may be many causes of this particular decline. It is possible that the opportunity for speakers to utter shall changed (for example, due to variation between text samples), rather than that shall declined in use when speakers had the opportunity. What we ideally wish to know is whether will is replacing shall in circumstances where the writer is in a position to choose. 4.3 Experimenting with shall/will alternants in DCPSE Our experimental data should ideally be restricted to include only cases in contexts where will and shall are interchangeable. In what follows we outline a number of knock-out contexts, attempting to focus on those cases where will and shall are true alternants and can therefore be said to represent a choice. In addition to declarative cases, shall and will can appear in interrogative and negative constructions. (1) a. Interrogatives: Shall we go to the park? vs Will we go to the park? b. Negatives: I won t/will not go to the park vs I shan t/shall not go to the park. However, the semantics of the interrogative cases are distinct from the declarative cases, different usage constraints may apply, or use may be sensitive to genre. Another concern is that the negative cases include the increasingly archaic and informal shan t. We therefore chose to concentrate on the base form in positive declarative utterances, and exclude these knock-out contexts. In Section 2 we discussed the fact that every text unit in DCPSE is given a tree analysis and we can use Fuzzy Tree Fragments (FTFs) to identify cases conforming to a particular structure. To extract declarative cases, we limit cases to where shall and will are classified as auxiliaries following a subject NP. This will retrieve from the corpus all cases of shall and will preceded by a pronoun or a noun phrase subject and exclude instances of subject auxiliary inversion. Figure 4 illustrates the FTF for finding declarative cases of shall, results for will are obtained by simply substituting the word. At this stage, the lexical slot for the subject NP is unspecified ( ), but we will revisit this later.

9 Figure 4: An FTF used to search for shall after any subject NP. A second, similar, FTF was used to retrieve instances of shall/will not and these cases were then subtracted from the results. We exclude all negative cases, including shall not/shan t/will not/won t. For now, we also exclude the contracted form ll. Results are summarised in Table 5a. We evaluate the alternation with a 2 2 χ 2 test. The χ 2 figures in the bottom row are equivalent to goodness of fit χ 2 tests against the total. 15 The final column contains three figures: the percentage swing d % from LLC to ICE-GB for shall out of the total (see Section 3), the 2 2 φ effect size measure (see Appendix 2) and the 2 2 χ 2 result. The results show a significant change between the two subcorpora, and that most of the variation over time appears, perhaps unsurprisingly, to be attributable to the decrease in the frequency of shall (note the high values in the χ 2 (shall) column). (spoken) shall will Total χ 2 (shall) χ 2 (will) summary LLC (1960s) d % = % ±19.67% ICE-GB (1990s) φ = 0.17 TOTAL 170 1,045 1, χ 2 = Table 5a: 2 2 χ 2 for shall and will between ICE-GB and LLC (spoken, positive and declarative; bold is significant for p<0.05). 16 The contracted form ll is excluded. If we analyse the figures for shall and will for British English presented by Mair and Leech (see Table 4) using the same method we obtain the results in Table 5b. (written) shall+ will+ ll Total χ 2 (shall+) χ 2 (will+ ll) summary LOB (1960s) 355 2,798 3, d % = % ±12.88% FLOB (1990s) 200 2,723 2, φ = 0.08 TOTAL 555 5,521 6, χ 2 = Table 5b: 2 2 χ 2 for shall (+shan t) and will (+ ll, won t) between LOB and FLOB (written), data from Mair and Leech. These results are significant, but the effect size measures (d % and φ) are lower than in our spoken data in Table 5a. 17 The question we might ask therefore, is, are the results significantly different? To answer this question we used a further test. Wallis (2010) defines a statistical separability test to compare the results of two 2 2 tests. 18 This finds that the results are significantly separable at 15 Values in bold are significant at p<0.05 (if they exceed 3.841). The figures on the bottom row indicate whether a particular value (shall, will etc.) significantly changes over time. The 2 2 result simply tells us that a change is taking place, but does not tell us where this is happening. High individual χ 2 values indicate cells which have unexpected values. 16 The contracted form ll and negative cases are excluded. Note that d % represents the percentage swing of shall. Cramér s φ is a similar measure, but is calculated across both shall and will it measures the size of the shall/will alternation (0 = no change over time and 1= complete change). It is particularly useful for comparing results. 17 In other words, the change is smaller, but still sufficiently large to be judged significant given the data available.

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